The Technique of Multivariate Empirical Mode Decomposition and Independent Component Analysis to Exactly Extract the Mismatch Negativity from EEG ⋆

By Song Li, Haifeng Li, Lin Ma and Lun Zhao

Abstract

Since the important rules of the mismatch negativity (MMN) in cognitive research, extraction of the MMN has a very important significance. In this study, a technique of multivariate empirical mode decomposition (MEMD) and independent component analysis (ICA) to extract the MMN from EEG in different frequency bands was proposed. After preprocessing, the multichannel EEG were decomposed into intrinsic mode functions (IMFs) with MEMD. According to the priori knowledge of MMN, three criterions were established for constructing event related potential (ERPs) which contained MMN from IMFs. For extracting clean MMN from ERPs, independent component analysis (ICA) was performed to separate MMN and P300 from the constructed ERPs. The optimal digital filtering (ODF) method was employed for comparing with the proposed technique on a dataset based on an auditory paradigm experiment. The results showed that the proposed technique can automatically extract cleaner MMN than ODF